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Springer Data-Driven Remaining Useful Life Prognosis Book

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Description

Master the complexities of stochastic degrading systems with this comprehensive guide from Springer. This book provides a systematic look at data-driven remaining useful life prognosis theory, making it an essential resource for professionals and researchers in reliability engineering. Learn how to transform condition monitoring data into actionable insights. The text explains how to predict the remaining useful life of systems to improve maintenance scheduling and logistics planning. By focusing on stochastic models and practical methods, this book bridges the gap between theoretical concepts and real-world applications. Whether you are managing industrial assets or studying reliability engineering, this volume offers the tools needed to handle degradation monitoring. It includes extensive experiment data and practical prognosis methods to help you implement effective predictive maintenance strategies in your own technical workflows.

Key Features

Systematic theory coverage that provides a detailed foundation for data-driven remaining useful life prognosis techniques.

Practical application of stochastic models to predict the life expectancy of degrading systems effectively.

Data-driven methods to help engineers utilize condition monitoring data for better maintenance and logistics planning.

Includes a wealth of degradation monitoring experiment data to support real-world learning and testing.

Focused approach on the methods and applications required for modern reliability engineering and system management.

Product Specifications

Format
hardcover
Domain
Amazon UK
Release Date
09 February 2017
Listed Since
26 October 2016

Barcode

No barcode data available

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